Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Novel knowledge-based mean force potential at the profile level.

Qiwen Dong1, Xiaolong Wang, Lei Lin

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, PR China. qwdong@insun.hit.edu.cn

BMC Bioinformatics
|June 29, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Chromosome-level genome assembly of the Amazonian native fish red-bellied pacu (Piaractus brachypomus).

Scientific data·2026
Same author

Prdm15 Deficiency Results in Impaired Hippocampal Neurogenesis and Autism-Like Behaviors.

Cellular and molecular neurobiology·2026
Same author

Bilateral Corneal Sensory and Nerve Structural Alterations in Unilateral Scleritis.

Cornea·2026
Same author

Development of a vascularized multi-organoid-on-a-chip to model the heart-islet axis in diabetic cardiomyopathy.

Bioactive materials·2026
Same author

UNG-RPA interaction governs the choice between high-fidelity and mutagenic uracil repair.

Nucleic acids research·2026
Same author

Deep cross-modal affective memory networks with adaptive multi-source heterogeneous transfer learning in speech emotion recognition.

Scientific reports·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

New profile-level potentials leverage evolutionary information for more accurate protein structure modeling. These novel statistical potentials outperform traditional residue-level methods in predicting protein structures and refining models.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modeling

Background:

  • Protein energetics modeling is crucial for understanding protein structure and function.
  • Knowledge-based mean force potentials are derived from experimental structures but typically operate at the atom or amino acid level.
  • Existing methods do not utilize evolutionary information present in protein profiles.

Purpose of the Study:

  • To develop novel knowledge-based mean force potentials at the profile level.
  • To incorporate evolutionary information from protein profiles into statistical potentials.
  • To enhance the accuracy of protein structure prediction and model refinement.

Main Methods:

  • Calculated frequency profiles from PSI-BLAST multiple sequence alignments.

Related Experiment Videos

  • Converted profiles into binary profiles using a probability threshold.
  • Developed four types of profile-level potentials: distance-dependent, contact, Phi/Psi dihedral angle, and accessible surface.
  • Evaluated potentials using fold assessment and decoy set recognition.

    • Main Results:

    • Profile-level potentials significantly outperform residue-level potentials in fold assessment and native structure recognition.
    • Distance-dependent and accessible surface profile potentials showed the most significant improvement (5-6%).
    • Distance-dependent profile-level potentials outperformed atom-level potentials in decoy set evaluation.
    • Profile-level potentials improved the performance of protein threading.

    Conclusions:

    • Profile-level knowledge-based mean force potentials offer superior discriminatory ability compared to residue-level potentials.
    • These novel potentials are valuable tools for improving protein structure prediction and model refinement.
    • The integration of evolutionary information enhances the predictive power of statistical potentials in bioinformatics.